Medical Image Analysis / Edition 2

by Atam P. Dhawan

Overview

The expanded and revised edition will split Chapter 4 to include more details and examples in FMRI, DTI, and DWI for MR image modalities. The book will also expand ultrasound imaging to 3-D dynamic contrast ultrasound imaging in a separate chapter.

A new chapter on Optical Imaging Modalities elaborating microscopy, confocal microscopy, endoscopy, optical coherent tomography, fluorescence and molecular imaging will be added. Another new chapter on Simultaneous Multi-Modality Medical Imaging including CT-SPECT and CT-PET will also be added. In the image analysis part, chapters on image reconstructions and visualizations will be significantly enhanced to include, respectively, 3-D fast statistical estimation based reconstruction methods, and 3-D image fusion and visualization overlaying multi-modality imaging and information. A new chapter on Computer-Aided Diagnosis and image guided surgery, and surgical and therapeutic intervention will also be added.

A companion site containing power point slides, author biography, corrections to the first edition and images from the text can be found here: wiley.com/public/sci_tech_med/medical_image/

Send an email to: [e-mail] to obtain a solutions manual. Please include your affiliation in your email.

Product Details

ISBN-13:	9780470622056
Publisher:	Wiley
Publication date:	03/01/2011
Series:	IEEE Press Series on Biomedical Engineering , #31
Pages:	400
Product dimensions:	6.30(w) x 9.30(h) x 1.20(d)

About the Author

ATAM P. DHAWAN, PHD, is Distinguished Professor in the Electrical and Computer Engineering Department at New Jersey Institute of Technology. He teaches courses in biomedical engineering and has supervised approximately fifty graduate students, including twenty-one PhD students. Dr. Dhawan is a Fellow of the IEEE and the recipient of numerous national and international awards. He has published more than 200 research articles in refereed journals, conference proceedings, and edited books. Dr. Dhawan has chaired numerous study sections and review panels for the National Institutes of Health in biomedical computing and medical imaging and health informatics. His current research interests are medical imaging, multi-modality medical image analysis, multi-grid image reconstruction, wavelets, genetic algorithms, neural networks, adaptive learning, and pattern recognition.

Read an Excerpt

Medical Image Analysis

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By Atam P. Dhawan

John Wiley & Sons

Copyright © 2003 The Institute of Electrical and Electronics Engineers
All right reserved.
ISBN: 0-471-45131-2

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Chapter One

Introduction

The last two decades have witnessed significant advances in medical imaging and computerized medical image processing. These advances have led to new two-, three- and multi-dimensional imaging modalities that have become important clinical tools in diagnostic radiology. The clinical significance of radiological imaging modalities in diagnosis and treatment of diseases is overwhelming. While planar X-ray imaging was the only radiological imaging method in the early part of the last century, several modern imaging modalities are in practice today to acquire anatomical, physiological, metabolic and functional information from the human body. The commonly used medical imaging modalities capable of producing multidimensional images for radiological applications are: X-ray Computed Tomography (X-ray CT), Magnetic Resonance Imaging (MRI), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) and Ultrasound. It should be noted that these modern imaging methods involve sophisticated instrumentation and equipment using high-speed electronics and computers for data collection, image reconstruction and display. Simple planar radiographic imaging methods such as chest X-rays and mammogramsusually provide images on a film that is exposed during imaging through an external radiation source (X-ray) and then developed to show images of body organs. These planar radiographic imaging methods provide high-quality analog images that are shadows or two-dimensional projected images of three-dimensional organs. On the other hand, recent complex medical imaging modalities such X-ray CT, MRI, SPECT, PET and Ultrasound depend heavily on computer technology for creation and display of digital images. Using the computer, multidimensional digital images of physiological structures can be processed and manipulated to visualize hidden characteristic diagnostic features that are difficult or impossible to see with planar imaging methods. Further, these features of interest can be quantified and analyzed using sophisticated computer programs and models to understand their behavior to help with a diagnosis or to evaluate treatment protocols. Nevertheless, the clinical significance of simple planar imaging methods such as X-ray radiographs (such as chest X-ray, mammograms, etc.) must not be underestimated, as they offer cost-effective and reliable screening tools that often provide important diagnostic information sufficient to make correct diagnosis and judgment about the treatment.

However, in many critical radiological applications, the multi-dimensional visualization and quantitative analysis of physiological structures provide unprecedented clinical information extremely valuable for diagnosis and treatment. The ability of computerized processing and analysis of medical imaging modalities provides a powerful tool to help physicians. Thus computer programs and methods to process and manipulate the raw data from medical imaging scanners must be carefully developed to preserve and enhance the real clinical information of interest rather than introducing additional artifacts. The ability to improve diagnostic information from medical images can be further enhanced by designing computer processing algorithms intelligently. Often, incorporating relevant knowledge about the physics of imaging, instrumentation and human physiology in computer programs provides outstanding improvement in image quality as well as analysis to help interpretation. For example, incorporating knowledge about the geometrical location of the source, detector and patient can reduce the geometric artifacts in the reconstructed images. Further, the use of geometrical locations and characteristic signatures in computer-aided enhancement, identification, segmentation and analysis of physiological structures of interest often improves the clinical interpretation of medical images.

1.1 MEDICAL IMAGING: A COLLABORATIVE PARADIGM

As discussed above, with the advent and enhancement of modern medical imaging modalities, intelligent processing of multi-dimensional images has become crucial in conventional or computer-aided interpretation for radiological and diagnostic applications. Medical imaging and processing in diagnostic radiology has evolved with significant contributions from a number of disciplines including mathematics, physics, chemistry, engineering, and medicine. This is evident when one sees a medical imaging scanner such as a MRI or PET scanner. The complexity of instrumentation and computer aided data collection and image reconstruction methods clearly indicate the importance of system integration as well as a critical understanding of the physics of imaging and image formation (see Fig. 1.1). Intelligent interpretation of medical images requires understanding of the interaction of the basic unit of imaging (such as protons in MRI, or X-ray photons in X-ray CT) in a biological environment, formation of a quantifiable signal representing the biological information, detection and acquisition of the signal of interest, and appropriate image reconstruction. In brief, intelligent interpretation and analysis of biomedical images require an understanding of the acquisition of images.

A number of computer vision methods have been developed for a variety of applications in image processing, segmentation, analysis and recognition. However, medical image reconstruction and processing require specialized knowledge of a specific medical imaging modality that is used to acquire images. The character of the collected data in the application environment (such as imaging the heart through MRI) should be properly understood for selecting or developing useful methods for intelligent image processing, analysis and interpretation. The use of application domain knowledge can provide useful help in selecting or developing the most appropriate image reconstruction and processing methods for accurate analysis and interpretation.

1.2 MEDICAL IMAGING MODALITIES

The field of medical imaging and image analysis has evolved due to the collective contributions from many areas of medicine, engineering and basic sciences. The overall objective of medical imaging is to acquire useful information about the physiological processes or organs of the body by using external or internal sources of energy. Figure 1.2 identifies medical imaging modalities classified on the basis of energy source used for imaging. Imaging methods available today for radiological applications may use external, internal or a combination of energy sources (Figure 1.3). In most commonly used imaging methods ionized radiation such as X-rays are used as an external energy source primarily for anatomical imaging. Such anatomical imaging modalities are based on the attenuation coefficient of radiation passing through the body. For example, X-ray radiographs and Computed Tomography (X-ray CT) imaging modalities measure attenuation coefficients of X-ray that are based on the density of the tissue or part of the body being imaged. The images of chest radiographs show a spatial distribution of X-ray attenuation coefficients reflecting the overall density variations of the anatomical parts in the chest. Another example of external energy source based imaging is ultrasound or acoustic imaging. Nuclear Medicine imaging modalities use an internal energy source through an emission process to image the human body. For emission imaging, radioactive pharmaceuticals are injected into the body to interact with selected body matter or tissue to form an internal source of radioactive energy that is used for imaging. The emission imaging principle is applied in Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). Such types of Nuclear Medicine imaging modalities provide useful metabolic information about the physiological functions of the organs. Further, a clever combination of external stimulation on internal energy sources can be used in medical imaging to acquire more accurate information about the tissue material and physiological responses and functions. Magnetic Resonance Imaging uses external magnetic energy to stimulate selected atomic nuclei such as hydrogen protons. The excited nuclei become the internal source of energy to provide electromagnetic signals for imaging through the process of relaxation. Magnetic Resonance Imaging of the human body provides high-resolution images of the human body with excellent soft-tissue characterization capabilities. Recent advances in MRI have led to perfusion and functional imaging aspects of human tissue and organs. Another emerging biophysiological imaging modality is fluorescence imaging, which uses an external ultraviolet energy source to stimulate the internal biological molecules of interest, which absorb the ultraviolet energy, become internal sources of energy and then emit the energy at visible electromagnetic radiation wavelengths.

Before a type of energy source or imaging modality is selected, it is important to understand the nature of physiological information needed for image formation. In other words, some basic questions about the information of interest should be answered. What information about the human body is needed? Is it anatomical, physiological or functional? What range of spatial resolution is acceptable? The selection of a specific medical imaging modality often depends on the type of suspected disease or localization needed for proper radiological diagnosis. For example, some neurological disorders and diseases demand very-high-resolution brain images for accurate diagnosis and treatment. On the other hand, full-body SPECT imaging to study metastasizing cancer does not require sub-millimeter imaging resolution. The information of interest here is cancer metastasis in the tissue that can be best obtained from the blood flow in the tissue or its metabolism. Breast imaging can be performed using X-rays, magnetic resonance, nuclear medicine or ultrasound. But the most effective and economical breast imaging modality so far has been X-ray mammography because of its simplicity, portability and low cost. One important source of radiological information for breast imaging is the presence and distribution of microcalcifications in the breast. This anatomical information can be obtained with high resolution using X-rays.

There is no perfect imaging modality for all radiological applications and needs. In addition, each medical imaging modality is limited by the corresponding physics of energy interactions with human body (or cells), instrumentation and often physiological constraints. These factors severely affect the quality and resolution of images sometimes making the interpretation and diagnosis very difficult. The performance of an imaging modality for a specific test or application is characterized by sensitivity and specificity factors. Sensitivity of a medical imaging test is defined primarily by its ability to detect true information. Let us suppose we have an X-ray imaging scanner for mammography. The sensitivity for imaging microcalcifications for a mammography scanner would depend on many factors including the X-ray wavelength used in the beam, intensity and polychromatic distribution of the input radiation beam, behavior of X-rays in breast tissue such as absorption and scattering coefficients, and film/detector efficiency to collect the output radiation. These factors would eventually affect the overall signal-to-noise ratio, leading to the loss of sensitivity of detecting microcalcifications. The specificity for a test depends on its ability to not detect the information when it is truly not there.

1.3 MEDICAL IMAGING: FROM PHYSIOLOGY TO INFORMATION PROCESSING

From physiology to image interpretation and information retrieval, medical imaging is a five-step paradigm. The five-step paradigm allows acquisition and analysis of useful information to understand the behavior of an organ or a physiological process.

1. Understanding Imaging Medium: The imaged objects (organs, tissues and specific pathologies) and associated physiological properties that could be used for obtaining signals suitable for the formation of an image must be studied for the selection of imaging instrumentation. This information is often very useful in designing image processing and analysis techniques for correct interpretation. The information about imaging medium may involve static or dynamic properties of the biological tissue. For example, tissue density is a static property that causes attenuation of an external radiation beam in X-ray imaging modality. Blood flow, perfusion and cardiac motion are examples of dynamic physiological properties that may alter the image of a biological entity. Due considerations of the dynamic behavior of the imaging medium is essential in designing compensation methods needed for correct image reconstruction and analysis. Motion artifacts pose serious limitations on data collection time and resolution in medical imaging instrumentation and therefore have a direct effect on the development of image processing methods.

2. Physics of Imaging: The next important consideration is the principle of imaging to be used for obtaining the data. For example, X-ray imaging modality uses transmission of X-rays through the body as the basis of imaging. On the other hand, in the nuclear medicine modality, Single Photon Emission Computed Tomography (SPECT) uses the emission of gamma rays resulting from the interaction of a radiopharmaceutical substance with the target tissue. The emission process and the energy range of gamma rays cause limitations on the resolution and data acquisition time for imaging. The associated methods for image formation in transmission and emission imaging modalities are so different that it is difficult to see the same level of anatomical information from both modalities. The SPECT and PET imaging modalities provide images that are poor in contrast and anatomical details while the X-ray CT imaging modality provides shaper images with high-resolution anatomical details. The MR imaging modality provides high-resolution anatomical details with excellent soft-tissue contrast.

3. Imaging Instrumentation: The instrumentation used in collecting the data is one of the most important factors defining the image quality in terms of signal-to-noise ratio, resolution and ability to show diagnostic information. Source specifications of the instrumentation directly effect imaging capabilities. In addition, detector responses such as non-linearity, low efficiency, long decay time and poor scatter rejection may cause artifacts in the image.

Continues...

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Table of Contents

Preface to the Second Edition xiii

Chapter 1 Introduction 1

1.1. Medical Imaging: A Collaborative Paradigm 2

1.2. Medical Imaging Modalities 3

1.3. Medical Imaging: from Physiology to Information Processing 6

1.3.1 Understanding Physiology and Imaging Medium 6

1.3.2 Physics of Imaging 7

1.3.3 Imaging Instrumentation 7

1.3.4 Data Acquisition and Image Reconstruction 7

1.3.5 Image Analysis and Applications 8

1.4. General Performance Measures 8

1.4.1 An Example of Performance Measure 10

1.5. Biomedical Image Processing and Analysis 11

1.6. Matlab Image Processing Toolbox 14

1.6.1 Digital Image Representation 14

1.6.2 Basic MATLAB Image Toolbox Commands 16

1.7. Imagepro Interface in Matlab Environment and Image Databases 19

1.7.1 Imagepro Image Processing Interface 19

1.7.2 Installation Instructions 20

1.8. Imagej and Other Image Processing Software Packages 20

1.9. Exercises 21

1.10. References 22

1.11. Definitions 22

Chapter 2 Image Formation23

2.1. Image Coordinate System 24

2.1.1 2-D Image Rotation 25

2.1.2 3-D Image Rotation and Translation Transformation 26

2.2. Linear Systems 27

2.3. Point Source and Impulse Functions 27

2.4. Probability and Random Variable Functions 29

2.4.1 Conditional and Joint Probability Density Functions 30

2.4.2 Independent and Orthogonal Random Variables 31

2.5. Image Formation 32

2.5.1 PSF and Spatial Resolution 35

2.5.2 Signal-to-Noise Ratio 37

2.5.3 Contrast-to-Noise Ratio 39

2.6. Pin-hole Imaging 39

2.7. Fourier Transform 40

2.7.1 Sinc Function 43

2.8. Radon Transform 44

2.9. Sampling 46

2.10. Discrete Fourier Transform 50

2.11. Wavelet Transform 52

2.12. Exercises 60

2.13. References 62

Chapter 3 Interaction of Electromagnetic Radiation with Matter in Medical Imaging 65

3.1. Electromagnetic Radiation 65

3.2. Electromagnetic Radiation for Image Formation 66

3.3. Radiation Interaction with Matter 67

3.3.1 Coherent or Rayleigh Scattering 67

3.3.2 Photoelectric Absorption 68

3.3.3 Compton Scattering 69

3.3.4 Pair Production 69

3.4. Linear Attenuation Coefficient 70

3.5. Radiation Detection 70

3.5.1 Ionized Chambers and Proportional Counters 70

3.5.2 Semiconductor Detectors 72

3.5.3 Advantages of Semiconductor Detectors 73

3.5.4 Scintillation Detectors 73

3.6. Detector Subsystem Output Voltage Pulse 76

3.7. Exercises 78

3.8. References 78

Chapter 4 Medical Imaging Modalities: X-Ray Imaging 79

4.1. X-Ray Imaging 80

4.2. X-Ray Generation 81

4.3. X-Ray 2-D Projection Imaging 84

4.4. X-Ray Mammography 86

4.5. X-Ray CT 88

4.6. Spiral X-Ray CT 92

4.7. Contrast Agent, Spatial Resolution, and SNR 95

4.8. Exercises 96

4.9. References 97

Chapter 5 Medical Imaging Modalities: Magnetic Resonance Imaging 99

5.1. MRI Principles 100

5.2. MR Instrumentation 110

5.3. MRI Pulse Sequences 112

5.3.1 Spin-Echo Imaging 114

5.3.2 Inversion Recovery Imaging 118

5.3.3 Echo Planar Imaging 119

5.3.4 Gradient Echo Imaging 123

5.4. Flow Imaging 125

5.5. fMRI 129

5.6. Diffusion Imaging 130

5.7. Contrast, Spatial Resolution, and SNR 135

5.8. Exercises 137

5.9. References 138

Chapter 6 Nuclear Medicine Imaging Modalities 139

6.1. Radioactivity 139

6.2. SPECT 140

6.2.1 Detectors and Data Acquisition System 142

6.2.2 Contrast, Spatial Resolution, and Signal-to-Noise Ratio in SPECT Imaging 145

6.3. PET 148

6.3.1 Detectors and Data Acquisition Systems 150

6.3.2 Contrast, Spatial Resolution, and SNR in PET Imaging 150

6.4. Dual-Modality Spect–CT and PET–CT Scanners 151

6.5. Exercises 154

6.6. References 155

Chapter 7 Medical Imaging Modalities: Ultrasound Imaging 157

7.1. Propagation of Sound in a Medium 157

7.2. Reflection and Refraction 159

7.3. Transmission of Ultrasound Waves in a Multilayered Medium 160

7.4. Attenuation 162

7.5. Ultrasound Reflection Imaging 163

7.6. Ultrasound Imaging Instrumentation 164

7.7. Imaging with Ultrasound: A-Mode 166

7.8. Imaging with Ultrasound: M-Mode 167

7.9. Imaging with Ultrasound: B-Mode 168

7.10. Doppler Ultrasound Imaging 169

7.11. Contrast, Spatial Resolution, and SNR 170

7.12. Exercises 171

7.13. References 172

Chapter 8 Image Reconstruction 173

8.1. Radon Transform and Image Reconstruction 174

8.1.1 The Central Slice Theorem 174

8.1.2 Inverse Radon Transform 176

8.1.3 Backprojection Method 176

8.2. Iterative Algebraic Reconstruction Methods 180

8.3. Estimation Methods 182

8.4. Fourier Reconstruction Methods 185

8.5. Image Reconstruction in Medical Imaging Modalities 186

8.5.1 Image Reconstruction in X-Ray CT 186

8.5.2 Image Reconstruction in Nuclear Emission Computed Tomography: SPECT and PET 188

8.5.2.1 A General Approach to ML–EM Algorithms 189

8.5.2.2 A Multigrid EM Algorithm 190

8.5.3 Image Reconstruction in Magnetic Resonance Imaging 192

8.5.4 Image Reconstruction in Ultrasound Imaging 193

8.6. Exercises 194

8.7. References 195

Chapter 9 Image Processing and Enhancement 199

9.1. Spatial Domain Methods 200

9.1.1 Histogram Transformation and Equalization 201

9.1.2 Histogram Modification 203

9.1.3 Image Averaging 204

9.1.4 Image Subtraction 204

9.1.5 Neighborhood Operations 205

9.1.5.1 Median Filter 207

9.1.5.2 Adaptive Arithmetic Mean Filter 207

9.1.5.3 Image Sharpening and Edge Enhancement 208

9.1.5.4 Feature Enhancement Using Adaptive Neighborhood Processing 209

9.2. Frequency Domain Filtering 212

9.2.1 Wiener Filtering 213

9.2.2 Constrained Least Square Filtering 214

9.2.3 Low-Pass Filtering 215

9.2.4 High-Pass Filtering 217

9.2.5 Homomorphic Filtering 217

9.3. Wavelet Transform for Image Processing 220

9.3.1 Image Smoothing and Enhancement Using Wavelet Transform 223

9.4. Exercises 226

9.5. References 228

Chapter 10 Image Segmentation 229

10.1. Edge-Based Image Segmentation 229

10.1.1 Edge Detection Operations 230

10.1.2 Boundary Tracking 231

10.1.3 Hough Transform 233

10.2. Pixel-Based Direct Classification Methods 235

10.2.1 Optimal Global Thresholding 237

10.2.2 Pixel Classification Through Clustering 239

10.2.2.1 Data Clustering 239

10.2.2.2 k-Means Clustering 241

10.2.2.3 Fuzzy c-Means Clustering 242

10.2.2.4 An Adaptive FCM Algorithm 244

10.3. Region-Based Segmentation 245

10.3.1 Region-Growing 245

10.3.2 Region-Splitting 247

10.4. Advanced Segmentation Methods 248

10.4.1 Estimation-Model Based Adaptive Segmentation 249

10.4.2 Image Segmentation Using Neural Networks 254

10.4.2.1 Backpropagation Neural Network for Classification 255

10.4.2.2 The RBF Network 258

10.4.2.3 Segmentation of Arterial Structure in Digital Subtraction Angiograms 259

10.5. Exercises 261

10.6. References 262

Chapter 11 Image Representation, Analysis, and Classification 265

11.1. Feature Extraction and Representation 268

11.1.1 Statistical Pixel-Level Features 268

11.1.2 Shape Features 270

11.1.2.1 Boundary Encoding: Chain Code 271

11.1.2.2 Boundary Encoding: Fourier Descriptor 273

11.1.2.3 Moments for Shape Description 273

11.1.2.4 Morphological Processing for Shape Description 274

11.1.3 Texture Features 280

11.1.4 Relational Features 282

11.2. Feature Selection for Classification 283

11.2.1 Linear Discriminant Analysis 285

11.2.2 PCA 288

11.2.3 GA-Based Optimization 289

11.3. Feature and Image Classification 292

11.3.1 Statistical Classification Methods 292

11.3.1.1 Nearest Neighbor Classifier 293

11.3.1.2 Bayesian Classifier 293

11.3.2 Rule-Based Systems 294

11.3.3 Neural Network Classifiers 296

11.3.3.1 Neuro-Fuzzy Pattern Classification 296

11.3.4 Support Vector Machine for Classification 302

11.4. Image Analysis and Classification Example: “Difficult-To-Diagnose” Mammographic Microcalcifications 303

11.5. Exercises 306

11.6. References 307

Chapter 12 Image Registration 311

12.1. Rigid-Body Transformation 314

12.1.1 Affine Transformation 316

12.2. Principal Axes Registration 316

12.3. Iterative Principal Axes Registration 319

12.4. Image Landmarks and Features-Based Registration 323

12.4.1 Similarity Transformation for Point-Based Registration 323

12.4.2 Weighted Features-Based Registration 324

12.5. Elastic Deformation-Based Registration 325

12.6. Exercises 330

12.7. References 331

Chapter 13 Image Visualization 335

13.1. Feature-Enhanced 2-D Image Display Methods 336

13.2. Stereo Vision and Semi-3-D Display Methods 336

13.3. Surface- and Volume-Based 3-D Display Methods 338

13.3.1 Surface Visualization 339

13.3.2 Volume Visualization 344

13.4. VR-Based Interactive Visualization 347

13.4.1 Virtual Endoscopy 349

13.5. Exercises 349

13.6. References 350

Chapter 14 Current and Future Trends in Medical Imaging and Image Analysis 353

14.1. Multiparameter Medical Imaging and Analysis 353

14.2. Targeted Imaging 357

14.3. Optical Imaging and Other Emerging Modalities 357

14.3.1 Optical Microscopy 358

14.3.2 Optical Endoscopy 360

14.3.3 Optical Coherence Tomography 360

14.3.4 Diffuse Reflectance and Transillumination Imaging 362

14.3.5 Photoacoustic Imaging: An Emerging Technology 363

14.4. Model-Based and Multiscale Analysis 364

14.5. References 366

Index 503